Simple outlier detection by comparing distances between examples.
Outliers widget first computes distances between each pair of examples in input Examples. Average distance between example to its nearest examples is valued by a Z-score. Z-scores higher than zero denote an example that is more distant to other examples than average. Input can also be a distance matrix: in this case precalculated distances are used.
Two parameters for Z-score calculation can be choosen: distance metrics and number of nearest examples to which example's average distance is computed. Also, minimum Z-score to consider an example as outlier can be set. Note, that higher the example's Z-score, more distant is the example from other examples.
Changes are applied automatically.
Below is a simple example how to use this widget. The input is fed directly from the File widget, and the output Examples with Z-score to the Data Table widget.